System for assessing cardiac condition

Abstract

A system for assessing a cardiac condition includes an electrocardiogram (ECG) sensor, a photoplethysmography (PPG) sensor, and a processing circuit. The ECG sensor obtains an ECG signal related to a user. The PPG sensor obtains a PPG signal related to the user. The processing circuit generates a cardiac assessment result based on the PPG signal sensed during a first time period and the ECG signal sensed during a second time period. The first time period is longer than the second time period.

What is claimed is: 
     
         1 . A system comprising:
 an electrocardiogram (ECG) sensor, configured to obtain an ECG signal related to a user;   a photoplethysmography (PPG) sensor, configured to obtain a PPG signal related to the user; and   a processing circuit, electrically connected to the ECG sensor and the PPG sensor and configured to generate a cardiac assessment result according to the PPG signal sensed during a first time period and the ECG signal sensed during a second time period, wherein the first time period is longer than the second time period.   
     
     
         2 . The system of  claim 1 , wherein the processing circuit is configured to input the ECG signal into a first machine learning model to generate a first feature vector,
 wherein the processing circuit is configured to detect a plurality of beats from the PPG signal, calculate a plurality of beat intervals according to the beats, and input the beat intervals into a second machine learning model to generate a second feature vector,   wherein the processing circuit is configured to input the first feature vector and the second feature vector into a third machine learning model to generate the cardiac assessment result.   
     
     
         3 . The system of  claim 2 , wherein the first time period comprises a plurality of sub-periods, the processing circuit is configured to select the beats sensed in a time segment from each of the sub-periods, and calculate the beat intervals according to the selected beats. 
     
     
         4 . The system of  claim 2 , wherein the first machine learning model is a convolutional neural network, the second machine learning model is a transformer, and the third machine learning model is a multilayer perceptron network. 
     
     
         5 . The system of  claim 1 , wherein the processing circuit is configured to recognize a plurality of R peaks in the ECG signal, calculate a plurality of RR intervals among the R peaks, and calculate a plurality of ECG features according to the RR intervals,
 wherein the processing circuit is configured to detect a plurality of beats from the PPG signal, calculate a plurality of beat intervals according to the beats, and calculate a plurality of PPG features according to the beat intervals,   wherein the processing circuit is configured to input the ECG features and the PPG features into a machine learning model to generate the cardiac assessment result.   
     
     
         6 . The system of  claim 5 , wherein the processing circuit is configured to recognize a plurality of cardiac cycles from the ECG signal, and align the cardiac cycles based on the R peaks to generate a template cycle,
 wherein the processing circuit is configured to recognize a template P peak, a template Q peak, a template R peak, a template S peak, and a template T peak in the template cycle,   wherein the processing circuit is configured to calculate a time difference between the template R peak and one of the template P peak, the template Q peak, the template S peak and the template T peak as one of the ECG features.   
     
     
         7 . The system of  claim 6 , wherein the processing circuit is configured to calculate an amplitude difference between a baseline amplitude and one of the template P peak, the template Q peak, the template R peak, the template S peak, and the template T peak as one of the ECG features. 
     
     
         8 . The system of  claim 7 , wherein the first time period comprises a plurality of sub-periods, the processing circuit is configured to select the beats sensed in a time segment from each of the sub-periods, and concatenate the selected beats to calculate the beat intervals. 
     
     
         9 . The system of  claim 1 , wherein the cardiac assessment result comprises a survival curve and a risk score, and the survival curve comprises a plurality of probabilities of not experiencing a heart failure on a time axis. 
     
     
         10 . The system of  claim 9 , wherein the processing circuit is configured to generate the risk score according to the probability at a predetermined time on the time axis.